



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a      :  List[Any]			     =			{
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Tuple			     =			[
	    'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GraphormerForGraphClassification',
	    'GraphormerModel',
	    'GraphormerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_graphormer import (
		    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GraphormerForGraphClassification,
		    GraphormerModel,
		    GraphormerPreTrainedModel,
		)


else:
	import sys

	a      :  Union[str, Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)











from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TFFunnelBaseModel,
	    TFFunnelForMaskedLM,
	    TFFunnelForMultipleChoice,
	    TFFunnelForPreTraining,
	    TFFunnelForQuestionAnswering,
	    TFFunnelForSequenceClassification,
	    TFFunnelForTokenClassification,
	    TFFunnelModel,
	)







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=13,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=99,   SCREAMING_SNAKE_CASE_=[1, 1, 2],   SCREAMING_SNAKE_CASE_=1,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=8,   SCREAMING_SNAKE_CASE_=37,   SCREAMING_SNAKE_CASE_="gelu_new",   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.1,   SCREAMING_SNAKE_CASE_=0.0,   SCREAMING_SNAKE_CASE_=512,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=0.0_2,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=4,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=False,   )       ->   Tuple:
						UpperCAmelCase_:		Any							      =     parent
						UpperCAmelCase_:		Optional[Any]							      =     batch_size
						UpperCAmelCase_:		Dict							      =     seq_length
						UpperCAmelCase_:		Union[str, Any]							      =     is_training
						UpperCAmelCase_:		Optional[Any]							      =     use_input_mask
						UpperCAmelCase_:		Optional[Any]							      =     use_token_type_ids
						UpperCAmelCase_:		int							      =     use_labels
						UpperCAmelCase_:		List[str]							      =     vocab_size
						UpperCAmelCase_:		Optional[int]							      =     block_sizes
						UpperCAmelCase_:		Tuple							      =     num_decoder_layers
						UpperCAmelCase_:		List[Any]							      =     d_model
						UpperCAmelCase_:		Dict							      =     n_head
						UpperCAmelCase_:		Optional[Any]							      =     d_head
						UpperCAmelCase_:		Optional[Any]							      =     d_inner
						UpperCAmelCase_:		str							      =     hidden_act
						UpperCAmelCase_:		str							      =     hidden_dropout
						UpperCAmelCase_:		Union[str, Any]							      =     attention_dropout
						UpperCAmelCase_:		Dict							      =     activation_dropout
						UpperCAmelCase_:		str							      =     max_position_embeddings
						UpperCAmelCase_:		Dict							      =     type_vocab_size
						UpperCAmelCase_:		str							      =     2
						UpperCAmelCase_:		Dict							      =     num_labels
						UpperCAmelCase_:		Optional[int]							      =     num_choices
						UpperCAmelCase_:		Optional[int]							      =     scope
						UpperCAmelCase_:		List[Any]							      =     initializer_std

						# Used in the tests to check the size of the first attention layer
						UpperCAmelCase_:		Tuple							      =     n_head
						# Used in the tests to check the size of the first hidden state
						UpperCAmelCase_:		Union[str, Any]							      =     self.d_model
						# Used in the tests to check the number of output hidden states/attentions
						UpperCAmelCase_:		str							      =     sum(self.block_sizes					) + (0 if base else self.num_decoder_layers)
						# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
						# the last hidden state of the first block (which is the first hidden state of the decoder).
						if not base:
									UpperCAmelCase_:		Dict							      =     self.num_hidden_layers + 2

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Dict							      =     ids_tensor([self.batch_size, self.seq_length],   self.vocab_size					)

						UpperCAmelCase_:		Dict							      =     None
						if self.use_input_mask:
									UpperCAmelCase_:		List[str]							      =     random_attention_mask([self.batch_size, self.seq_length]					)

						UpperCAmelCase_:		Union[str, Any]							      =     None
						if self.use_token_type_ids:
									UpperCAmelCase_:		List[Any]							      =     ids_tensor([self.batch_size, self.seq_length],   self.type_vocab_size					)

						UpperCAmelCase_:		Any							      =     None
						UpperCAmelCase_:		str							      =     None
						UpperCAmelCase_:		Any							      =     None
						if self.use_labels:
									UpperCAmelCase_:		Dict							      =     ids_tensor([self.batch_size],   self.type_sequence_label_size					)
									UpperCAmelCase_:		Optional[Any]							      =     ids_tensor([self.batch_size, self.seq_length],   self.num_labels					)
									UpperCAmelCase_:		str							      =     ids_tensor([self.batch_size],   self.num_choices					)

						UpperCAmelCase_:		Dict							      =     FunnelConfig(
						    vocab_size=self.vocab_size,   block_sizes=self.block_sizes,   num_decoder_layers=self.num_decoder_layers,   d_model=self.d_model,   n_head=self.n_head,   d_head=self.d_head,   d_inner=self.d_inner,   hidden_act=self.hidden_act,   hidden_dropout=self.hidden_dropout,   attention_dropout=self.attention_dropout,   activation_dropout=self.activation_dropout,   max_position_embeddings=self.max_position_embeddings,   type_vocab_size=self.type_vocab_size,   initializer_std=self.initializer_std,   )

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   int:
						UpperCAmelCase_:		str							      =     TFFunnelModel(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     [input_ids, input_mask]
						UpperCAmelCase_:		Dict							      =     model(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.d_model)					)

						UpperCAmelCase_:		Union[str, Any]							      =     False
						UpperCAmelCase_:		int							      =     TFFunnelModel(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.d_model)					)

						UpperCAmelCase_:		Optional[Any]							      =     False
						UpperCAmelCase_:		str							      =     TFFunnelModel(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.d_model)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   Any:
						UpperCAmelCase_:		Dict							      =     TFFunnelBaseModel(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Optional[int]							      =     [input_ids, input_mask]
						UpperCAmelCase_:		List[str]							      =     model(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		int							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, 2, self.d_model)					)

						UpperCAmelCase_:		List[str]							      =     False
						UpperCAmelCase_:		str							      =     TFFunnelBaseModel(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, 3, self.d_model)					)

						UpperCAmelCase_:		List[Any]							      =     False
						UpperCAmelCase_:		List[Any]							      =     TFFunnelBaseModel(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, 2, self.d_model)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   Dict:
						UpperCAmelCase_:		List[Any]							      =     TFFunnelForPreTraining(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   str:
						UpperCAmelCase_:		Union[str, Any]							      =     TFFunnelForMaskedLM(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						UpperCAmelCase_:		List[Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   int:
						UpperCAmelCase_:		Tuple							      =     self.num_labels
						UpperCAmelCase_:		Optional[int]							      =     TFFunnelForSequenceClassification(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Union[str, Any]							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						UpperCAmelCase_:		Dict							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   int:
						UpperCAmelCase_:		Tuple							      =     self.num_choices
						UpperCAmelCase_:		List[str]							      =     TFFunnelForMultipleChoice(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_,   1					),   (1, self.num_choices, 1)					)
						UpperCAmelCase_:		Any							      =     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_,   1					),   (1, self.num_choices, 1)					)
						UpperCAmelCase_:		int							      =     tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_,   1					),   (1, self.num_choices, 1)					)
						UpperCAmelCase_:		Tuple							      =     {
						    """input_ids""": multiple_choice_inputs_ids,
						    """attention_mask""": multiple_choice_input_mask,
						    """token_type_ids""": multiple_choice_token_type_ids,
						}
						UpperCAmelCase_:		Tuple							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   int:
						UpperCAmelCase_:		List[Any]							      =     self.num_labels
						UpperCAmelCase_:		Dict							      =     TFFunnelForTokenClassification(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Tuple							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.seq_length, self.num_labels)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   )       ->   str:
						UpperCAmelCase_:		Any							      =     TFFunnelForQuestionAnswering(config=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
						UpperCAmelCase_:		Union[str, Any]							      =     model(SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.start_logits.shape,   (self.batch_size, self.seq_length)					)
						self.parent.assertEqual(result.end_logits.shape,   (self.batch_size, self.seq_length)					)

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		Optional[Any]							      =     self.prepare_config_and_inputs()
						(
						    (
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							(
						    UpperCAmelCase_
						)      ,							
						):		Tuple							      =     config_and_inputs
						UpperCAmelCase_:		Tuple							      =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict







@require_tf
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (
			        TFFunnelModel,
			        TFFunnelForMaskedLM,
			        TFFunnelForPreTraining,
			        TFFunnelForQuestionAnswering,
			        TFFunnelForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			A      		=	(
			    {
			        '''feature-extraction''': (TFFunnelBaseModel, TFFunnelModel),
			        '''fill-mask''': TFFunnelForMaskedLM,
			        '''question-answering''': TFFunnelForQuestionAnswering,
			        '''text-classification''': TFFunnelForSequenceClassification,
			        '''token-classification''': TFFunnelForTokenClassification,
			        '''zero-shot''': TFFunnelForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			A      		=	False
			A      		=	False

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Union[str, Any]							      =     TFFunnelModelTester(self					)
						UpperCAmelCase_:		List[str]							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						self.config_tester.run_common_tests()

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Tuple							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_					)







@require_tf
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(
			    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
			)
			A      		=	False
			A      		=	False

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		List[Any]							      =     TFFunnelModelTester(self,   base=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Dict							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						self.config_tester.run_common_tests()

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_base_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Union[str, Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_					)






